Deep Learning Assisted Robust Detection Techniques for a Chipless RFID Sensor Tags

In this paper, we present a new approach for robust reading of identification and sensor data from chipless RFID sensor tags. For the first time, Machine Learning (ML) and Deep Learning (DL) regression modelling techniques are applied to a dataset of measured Radar Cross Section (RCS) data that has been derived from large-scale robotic measurements of custom-designed, 3-bit chipless RFID sensor tags. The robotic system is implemented using the first-of-its-kind automated data acquisition method using an ur16e industry-standard robot. A large data set of 9,600 Electromagnetic (EM) RCS signatures collected using the automated system is used to train and validate four ML models and four 1-dimensional Convolutional Neural Network (1D CNN) architectures. For the first time, we report an end-to-end design and implementation methodology for robust detection of identification (ID) and sensing data using ML/DL models. Also, we report, for the first time, the effect of varying tag surface shapes, tilt angles, and read ranges that were incorporated into the training of models for robust detection of ID and sensing values. The results show that all the models were able to generalise well on the given data. However, the 1D CNN models outperformed the conventional ML models in the detection of ID and sensing values. The best 1D CNN model architectures performed well with a low Root Mean Square Error (RSME) of 0.061 (0.87%) for tag ID and 0.0241 (3.44%) error for the capacitive sensing

Advancements and artificial intelligence approaches in antennas for environmental sensing

Environmental sensors have come a long way over the last decade, surged in variety and capabilities. Such growth was impossible without developing wireless technologies, particularly antennas, thanks to advanced numerical computation software and artificial intelligence (AI). Sensors have numerous applications in industrial environments for purposes such as safety improvement, data acquisition, and environment and human body monitoring. For wireless sensor networks (WSNs), there may be several antennas to send the sensing data. However, further developments in the invention of planar antennas have opened up an unprecedented direction in the miniaturization of wireless sensors. Consequently, unobtrusive human-centric wireless sensing is becoming far more accessible due to the recent developments of epidermal antennas. Moreover, AI and its integration into antenna designs have resulted in more efficient WSNs. This chapter reviews the printed antennas for WSNs, explains how printed antenna sensors can be used for material characterization, gives an overview of epidermal antenna for unobtrusive human-centric wireless communications and sensing, and finally reviews the recent AI-based approaches in designing antennas

Tunable terahertz filter/antenna-sensor using graphene-based metamaterials

In this paper, a novel tunable graphene-based bandstop filter/antenna-sensor is presented. This structure is an integrated module that can be used to combine filtering and high-gain radiation performance. The initial design of the unit cell consists of four U-shaped stubs loaded, resembling the arms of a ring and a sensing layer in the substrate. The reflection and transmission spectra are obtained for various graphene’s chemical potentials and refractive index of sensing layer (Ns) of structure in the range of 1.3–1.6 THz. The proposed structure exhibits the attributes of both dual-band filter and single-band antenna-sensor. The conductivity of graphene and its structural parameters are studied to optimize the component performance. In filtering mode, the first bandstop is from 1.23 to 1.6 THz equal to 26% of fractional bandwidth (FBW) at 1.415 THz. The second stopband is centered at 3.12 THz with FBW of 14% for Ns = 1.6 and 0.6 eV chemical potential. In the antenna mode, a single band of the antenna-sensor is centered at 1.95 THz for the same Ns and same chemical potential. It is shown that a sensitivity of 0.145 THz/RIU is achieved at Ns = 1.5 and chemical potential of 0.6 eV. Additionally, the performance of the proposed filter/antenna-sensor module is investigated for different wave polarizations and oblique angles

A compact lowpass filter for satellite communication systems based on transfer function analysis

This paper presents a very efficient design procedure for a high-performance microstrip lowpass filter (LPF). Unlike many other sophisticated design methodologies of microstrip LPFs, which contain complicated configurations or even over-engineering in some cases, this paper presents a straightforward design procedure to achieve some of the best performance of this class of microstrip filters. The proposed filter is composed of three different polygonal-shaped resonators, two of which are responsible for stopband improvement, and the third resonator is designed to enhance the selectivity of the filter. A holistic performance assessment of the proposed filter is presented using a Figure of Merit (FOM) and compared with some of the best filters from the same class, highlighting the superiority of the proposed design. A prototype of the proposed filter was fabricated and tested, showing a 3-dB cut-off frequency (fc) at 1.27 GHz, having an ultrawide stopband with a suppression level of 25 dB, extending from 1.6 to 25 GHz. The return loss and the insertion loss of the passband are better than 20 dB and 0.25 dB, respectively. The fabricated filter has a high FOM of 76331, and its lateral size is 22.07 mm x 7.57 mm